SpecPCM: A Low-Power PCM-Based In-Memory Computing Accelerator for Full-Stack Mass Spectrometry Analysis by Keming Fan, Ashkan Moradifirouzabadi, Xiangjin Wu, Zheyu Li, Flavio Ponzina, Anton Persson, Eric Pop, Tajana Rosing, Mingu Kang

“…Our approach integrates contributions across multiple levels: application, algorithm, circuit, device, and instruction sets. We leverage a robust hyperdimensional computing (HD) algorithm with a novel dimension-packing method and develop specialized hardware for the end-to-end MS pipeline to overcome the nonideal behavior of PCM devices. …”
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Research on the integrated "cloud-edge-end" intelligent and precise management and control technology system for coal mine disasters by LIU Yubing, LI Yiteng, LI Zhonghui, YIN Shan, JING Chao, LI Kai, LI Zhenxing, ZHAO Shenglei, LIU Chengfei

“…On the cloud side, digital twin technology was applied for visual simulation, while coal mine major disaster safety situation analysis was conducted using Delphi theory and deep learning models. …”
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Joint Distribution pada Weighted Majority Vote (WMV) untuk Peningkatan Kinerja Sentiment Analysis Tersupervisi pada Dataset Twitter by Bagus Setya Rintyarna

“…Most of the sentiment analysis on the Indonesian language Twitter dataset relies on a single machine learning algorithm. This study combines the performance of various algorithms/experts while reducing the level of misclassification by updating the weights dynamically using a joint distribution-based weighted majority vote (WMV) from the Bayesian Network. …”
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Peningkatan Performa Ensemble Learning pada Segmentasi Semantik Gambar dengan Teknik Oversampling untuk Class Imbalance by Arie Nugroho, M. Arief Soeleman, Ricardus Anggi Pramunendar, Affandy Affandy, Aris Nurhindarto

“…The use of synthetic oversampling techniques can improve the level of accuracy in minority classes. The random forest algorithm experienced an increase in accuracy by 37% while the LGBM algorithm increased by 41%. …”
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Network-based machine learning reveals cardiometabolic multimorbidity patterns and modifiable lifestyle factors: a community-focused analysis of NHANES 2015–2018 by Danhui Mao, Junfang Mu, Yajing Li, Lu He, Qianhui Chai, Xin Zhao, Xiaojun Ren, Hui Cheng

“…Among the machine learning algorithms, Logistic Regression exhibited the best performance, with an Accuracy of 0.954 and an AUC-ROC area of 0.998. …”
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Smart agriculture: utilizing machine learning and deep learning for drought stress identification in crops by Tariq Ali, Saif Ur Rehman, Shamshair Ali, Khalid Mahmood, Silvia Aparicio Obregon, Rubén Calderón Iglesias, Tahir Khurshaid, Imran Ashraf

“…Notably, the accuracy of SVM was 82%, while gradient boosting and RNN showed 96%, and 94%, respectively and LSTM obtained an astounding 97% accuracy. …”
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Generalized Logistic Function: A Retracker to Improve the Accuracy of Water Level Time Series in Coastal Areas and Lakes by Arash Amini, Behzad Voosoghi, Mohammad Amin Khalili, Diego Di Martire, Shirzad Roohi, Mahmoud Pirooznia

“…The results demonstrate the superiority of the GLFA over the GLFN in coastal areas, while over lakes, the results are nearly equivalent. …”
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An Automated and Efficient Slope Unit Division Method Coupled with Computer Graphics and Hydrological Principles by Ting Xiao, Li Zhu, Lichang Wang, Beibei Yang, Can Wang, Haipeng Yao

“…This framework achieves automated slope unit delineation without thresholds while enabling multi-attribute fusion optimization, effectively addressing the shortcomings of HPAM. …”
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Personalized metronomic radiopharmaceutical therapy through injection profile optimization via physiologically based pharmacokinetic (PBPK) modeling by Aryan Golzaryan, M. Soltani, Farshad Moradi Kashkooli, Babak Saboury, Arman Rahmim

“…We designed a treatment algorithm to select optimal regimens with high AD, while investigating what we term radiopharmaceutical delivery payload (RDP). …”
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Simulation-Based Analysis of DRL Exploitation Rate for Voltage and Frequency Stability in a Single-Inverter Microgrid Control System by Noer Fadzri Perdana Dinata, Makbul Anwari Muhammad Ramli, Muhammad Irfan Jambak, Prisma Megantoro, Muhammad Abu Bakar Sidik

“…A comparison with a traditional Proportional-Integral-Derivative (PID) controller reveals that while the DRL agent with 2%-FER demonstrates strong performance in managing transient events, the PID controller provides superior stability in steady-state operation. …”
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Comparative Analysis of Resampling Techniques for Class Imbalance in Financial Distress Prediction Using XGBoost by Guodong Hou, Dong Ling Tong, Soung Yue Liew, Peng Yin Choo

“…Results show that standard Synthetic Minority Oversampling Technique (SMOTE) enhanced F1-score (up to 0.73) and Matthews Correlation Coefficient (MCC, up to 0.70), while SMOTE-Tomek and Borderline-SMOTE further boosted recall, slightly sacrificing precision. …”
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Analysis of the Trends and Driving Factors of Cultivated Land Utilization Efficiency in Henan Province from 2000 to 2020 by Henggang Zhang, Chenhui Zhu, Tianyu Jiao, Kaiyue Luo, Xu Ma, Mingyu Wang

“…Additionally, we used a genetic algorithm optimized Artificial Neural Network (ANN) and a particle swarm optimization-based Random Forest (RF) model to assess the comprehensive in-fluence between topography, climate, and human activities on CLUE, in which incorporating Shapley Additive Explanations (SHAP) values. …”
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Predicting iron contents in the Tamra-Douahria mining site using a deep neural network approach by Zohra Kraiem, Fathi Maalaoui, Salah Bouhlel, Chaouki Sadik, Ali Sdiri

“…Out of 357, 285 data sets were selected for training the algorithm while 72 data points were used for model testing and validation. …”
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AI-Assisted Design of Drain-Extended FinFET With Stepped Field Plate for Multi-Purpose Applications by Xiaoyun Huang, Hongyu Tang, Chenggang Xu, Yuxuan Zhu, Yan Pan, Dawei Gao, Yitao Ma, Kai Xu

“…Moreover, within an AI-assisted design framework, predictive modeling and multi-objective optimization of the device are accomplished using Kolmogorov–Arnold Networks (KAN) and the Nondominated Sorting Genetic Algorithm (NSGA-III). More importantly, the proposed framework enables efficient device design and performance evaluation, achieving an average prediction accuracy of 98.19% for electrical performance metrics while being over two million times faster than traditional Technology Computer-Aided-Design (TCAD) simulations. …”
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Machine learning model to predicting synergy of ultrasonication and solvation impacts on crude oil viscosity by Nasir Khan, Mehdi Razavifar, Qazi Adnan Ahmad, Muhammad Siyar, Masoud Riazi, Waqar Khan, Jafar Qajar

“…In this study, we develop a machine learning-based algorithm to rigorously predict the synergistic effects of ultrasonication and solvation on crude oil viscosity. …”
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Disrupted fetal carbohydrate metabolism in children with autism spectrum disorder by Serena B. Gumusoglu, Brandon M. Schickling, Donna A. Santillan, Lynn M. Teesch, Mark K. Santillan

“…Untargeted principle components analysis of all metabolites did not reveal group differences, while targeted biomarker assessment (using only Fructose 6-phosphate, D-Mannose, and D-Fructose) by a Random Forest algorithm generated an area under the curve (AUC) = 0.766 (95% CI: 0.612–0.896) for ASD prediction. …”
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Machine learning prediction of pKa of organic acids by Juda Baikété, Alhadji Malloum, Jeanet Conradie

“…The model trained with the ExTr algorithm showed the best prediction performance with an overall mean absolute error (MAE) value of 1.41 pKa units. …”
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Using self-generated identification codes to match anonymous longitudinal data in a sexual health study of secondary school students: a cohort study by Edmond Pui Hang Choi, Ellie Bostwick Andres, Heidi Sze Lok Fan, Lai Ming Ho, Alice Wai Chi Fung, Kevin Wing Chung Lau, Neda Hei Tung Ng, Monique Yeung, Janice Mary Johnston

“…These findings highlight the potential of SGICs as a tool for longitudinal data matching while underscoring the need for further refinement of code generation processes and matching algorithms to minimize data wastage and improve effectiveness.…”
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Artificial intelligence in dentistry—A review by Hao Ding, Jiamin Wu, Wuyuan Zhao, Jukka P. Matinlinna, Jukka P. Matinlinna, Michael F. Burrow, James K. H. Tsoi

“…Evidence-based dentistry (EBD) is regarded as the gold standard for decision making by dental professionals, while AI machine learning (ML) models learn from human expertise. …”
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Is there a competitive advantage to using multivariate statistical or machine learning methods over the Bross formula in the hdPS framework for bias and variance estimation? by Mohammad Ehsanul Karim, Yang Lei

“…Rare outcome scenarios generally resulted in lower MSE and better precision, while rare exposure scenarios were associated with higher bias and MSE. …”
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